When assessing the value of enterprise data it is imperative to include all aspects of the master data asset.
In many industries, the value of enterprise data can indicate the value of the business itself. Therefore, when assessing the total value of all enterprise data it is vital to include all stored master data. Much of this data plays a central role in everyday operations – however, it is also important to analyze data that the business utilizes less frequently.
Dark Data is the lost, unused "leftover" data that all companies have and never take into consideration – despite the fact that they may have tremendous current or potential value. These are part of the wider idea that all data has value , regardless of structure or content, and that companies just need to find out how to unlock their potential.
Notionally, all data has worth, regardless of its structure and content. Subsequently, businesses need to implement a comprehensive data project to maximize value extracted from master data. These initiatives will uncover ‘dark’ or ‘hidden’ data. All businesses have it, but many do not account for it despite the fact that often, it has enormous value. Transforming data into profit necessitates research into governance, storage, integration, data hygiene, and analytics.
Data storage and business intelligence
Big data technology has made significant advancements in the last few years. This has led to a renewed interest in the profitability of dark data. Dark data lurks in numerous locations – and when undertaking a dark data project, many companies may be surprised by the influx of additional information. Traditional sources generate data, as well as siloed systems, underused servers, and personal desktops. Gathering this disparate intelligence provokes questions about storage. For instance, should the organization store the information on hardware or in a cloud storage system? From here, businesses need to consider how to analyze this information. However, before implementing an analytics project, the team need to identify their objectives.
Data governance and hygiene
Dark data governance and hygiene intersects with master data governance. Previously, it has been proven that organizations must have a governance strategy in place for both structured and unstructured data, as even the most experienced data scientists find it difficult to predict when the latter will prove useful. Like big data, dark or hidden data reiterates the need for a general data hygiene strategy, especially in light of new data protection legislation. Depending on the sector, there are many new regulations as to how long a company can retain customer information in master data stores.
Extracting profit
Extracting profit from dark data requires a thorough strategy. Most importantly, businesses need to identify a specific use-value for analytics. Often, this opportunity to monetize data will not immediately present itself. Consequently, businesses should establish a dedicated working group to identify a targeted strategy to generate profit from dark data analytics.
Add additional value to master data
Big data analytics are rapidly becoming mainstream and the volume of data available to businesses is increasing exponentially. In light of this, what makes dark data such a valuable resource is that such a large proportion is underutilized. Businesses should recognize that if they can monetize such a small percentage of their master data asset, it is more than likely they can generate significant profit by undertaking a dark data initiative. As such, dark data can add substantial value to enterprise data – and therefore, the enterprise as a whole.
